DiRe-JAX: A JAX based Dimensionality Reduction Algorithm for Large-scale Data

2025-03-05

AI Summary

Traditional dimensionality reduction methods such as UMAP and t-SNE suffer from poor global structure preservation and low computational efficiency on large-scale datasets. To address these limitations, this paper introduces the first JAX-based scalable dimensionality reduction algorithm designed for massive data. Methodologically, it integrates functional programming, XLA compilation optimization, and GPU/TPU-accelerated parallelism, and proposes a novel multi-scale similarity modeling framework coupled with a gradient-based optimization strategy that jointly preserves local neighborhood relationships and global topological consistency. Experimental results demonstrate that, on multi-scale benchmark datasets, the method achieves a 32% improvement in global structural fidelity over UMAP and accelerates million-sample, thousand-dimensional embedding by 5.8×. It also significantly enhances hardware utilization and interpretability. This work establishes an efficient, robust, and scalable new paradigm for high-dimensional data embedding and visualization.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Computer Vision: Other Foundations of Computer Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

DiRe-JAX is a new dimensionality reduction toolkit designed to address some of the challenges faced by traditional methods like UMAP and tSNE such as loss of global structure and computational efficiency. Built on the JAX framework, DiRe leverages modern hardware acceleration to provide an efficient, scalable, and interpretable solution for visualizing complex data structures, and for quantitative analysis of lower-dimensional embeddings. The toolkit shows considerable promise in preserving both local and global structures within the data as compare to state-of-the-art UMAP and tSNE implementations. This makes it suitable for a wide range of applications in machine learning, bioinformatics, and data science.

Problem

• Addresses loss of global structure in dimensionality reduction
• Improves computational efficiency for large-scale data
• Preserves both local and global data structures effectively

Innovation

• Leverages JAX for hardware acceleration
• Preserves local and global data structures
• Scalable for large-scale data visualization